

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__a    :List[str]      =			logging.get_logger(__name__)

__a    :Tuple      =			{
    'microsoft/table-transformer-detection': (
        'https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'
    ),
}

class   _a					(			snake_case_				):




      """simple docstring"""

      _lowerCamelCase       :			Dict      		=       'table-transformer'
      _lowerCamelCase       :			Any      		=       ['past_key_values']
      _lowerCamelCase       :			Optional[int]      		=       {
          'hidden_size': 'd_model',
          'num_attention_heads': 'encoder_attention_heads',
      }





      def __init__(   self							:				Dict						,			UpperCAmelCase							:				str=True						,			UpperCAmelCase							:				str=None						,			UpperCAmelCase							:				Optional[int]=3						,			UpperCAmelCase							:				List[str]=100						,			UpperCAmelCase							:				Union[str, Any]=6						,			UpperCAmelCase							:				Optional[int]=2048						,			UpperCAmelCase							:				Any=8						,			UpperCAmelCase							:				Union[str, Any]=6						,			UpperCAmelCase							:				str=2048						,			UpperCAmelCase							:				Dict=8						,			UpperCAmelCase							:				Tuple=0.0						,			UpperCAmelCase							:				Tuple=0.0						,			UpperCAmelCase							:				int=True						,			UpperCAmelCase							:				Any="relu"						,			UpperCAmelCase							:				int=256						,			UpperCAmelCase							:				int=0.1						,			UpperCAmelCase							:				Tuple=0.0						,			UpperCAmelCase							:				str=0.0						,			UpperCAmelCase							:				str=0.02						,			UpperCAmelCase							:				List[str]=1.0						,			UpperCAmelCase							:				Union[str, Any]=False						,			UpperCAmelCase							:				Dict="sine"						,			UpperCAmelCase							:				List[str]="resnet50"						,			UpperCAmelCase							:				str=True						,			UpperCAmelCase							:				Optional[Any]=False						,			UpperCAmelCase							:				Union[str, Any]=1						,			UpperCAmelCase							:				Union[str, Any]=5						,			UpperCAmelCase							:				List[Any]=2						,			UpperCAmelCase							:				Dict=1						,			UpperCAmelCase							:				Tuple=1						,			UpperCAmelCase							:				Tuple=5						,			UpperCAmelCase							:				Optional[Any]=2						,			UpperCAmelCase							:				Optional[int]=0.1						,			**UpperCAmelCase							:				int						,			):
            if backbone_config is not None and use_timm_backbone:
                  raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`."	)

            if not use_timm_backbone:
                  if backbone_config is None:
                        logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."	)
                        A_						  =				CONFIG_MAPPING["resnet"](out_features=["stage4"]	)
                  elif isinstance(UpperCAmelCase						,			UpperCAmelCase	):
                        A_						  =				backbone_config.get("model_type"	)
                        A_						  =				CONFIG_MAPPING[backbone_model_type]
                        A_						  =				config_class.from_dict(UpperCAmelCase	)
                  # set timm attributes to None
                  A_  ,     A_  ,     A_						  =				None, None, None

            A_						  =				use_timm_backbone
            A_						  =				backbone_config
            A_						  =				num_channels
            A_						  =				num_queries
            A_						  =				d_model
            A_						  =				encoder_ffn_dim
            A_						  =				encoder_layers
            A_						  =				encoder_attention_heads
            A_						  =				decoder_ffn_dim
            A_						  =				decoder_layers
            A_						  =				decoder_attention_heads
            A_						  =				dropout
            A_						  =				attention_dropout
            A_						  =				activation_dropout
            A_						  =				activation_function
            A_						  =				init_std
            A_						  =				init_xavier_std
            A_						  =				encoder_layerdrop
            A_						  =				decoder_layerdrop
            A_						  =				encoder_layers
            A_						  =				auxiliary_loss
            A_						  =				position_embedding_type
            A_						  =				backbone
            A_						  =				use_pretrained_backbone
            A_						  =				dilation
            # Hungarian matcher
            A_						  =				class_cost
            A_						  =				bbox_cost
            A_						  =				giou_cost
            # Loss coefficients
            A_						  =				mask_loss_coefficient
            A_						  =				dice_loss_coefficient
            A_						  =				bbox_loss_coefficient
            A_						  =				giou_loss_coefficient
            A_						  =				eos_coefficient
            super().__init__(is_encoder_decoder=UpperCAmelCase						,			**UpperCAmelCase	)





      @property
      def 		__A	(   self							:				int	):
            return self.encoder_attention_heads







      @property
      def 		__A	(   self							:				int	):
            return self.d_model




class   _a					(			snake_case_				):




      """simple docstring"""

      _lowerCamelCase       :			Union[str, Any]      		=       version.parse('1.11'				)





      @property
      def 		__A	(   self							:				Any	):
            return OrderedDict(
                [
                    ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
                    ("pixel_mask", {0: "batch"}),
                ]	)





      @property
      def 		__A	(   self							:				Any	):
            return 1E-5







      @property
      def 		__A	(   self							:				List[Any]	):
            return 12






"""simple docstring"""





import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
       import torch

       from transformers import AutoModelForImageClassification

if is_vision_available():
       from transformers import AutoImageProcessor

@require_torch
@require_vision
class 						lowercase   (   unittest.TestCase   ):





      @slow
      def        _snake_case     (			self					)						->	Any:
            lowerCAmelCase    		=			AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""					)
            lowerCAmelCase    		=			AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""					)
            model.to(lowercase					)

            from datasets import load_dataset

            lowerCAmelCase    		=			load_dataset("""nielsr/rvlcdip-demo"""					)

            lowerCAmelCase    		=			dataset["""train"""][0]["""image"""].convert("""RGB"""					)

            lowerCAmelCase    		=			image_processor(lowercase ,		return_tensors="""pt"""					).to(lowercase					)

            # forward pass
            with torch.no_grad():
                  lowerCAmelCase    		=			model(**lowercase					)
                  lowerCAmelCase    		=			outputs.logits

            lowerCAmelCase    		=			torch.Size((1, 16)					)
            self.assertEqual(logits.shape ,		lowercase					)

            lowerCAmelCase    		=			torch.tensor(
                [-0.4_158, -0.4_092, -0.4_347] ,		device=lowercase ,		dtype=torch.float ,		)
            self.assertTrue(torch.allclose(logits[0, :3] ,		lowercase ,		atol=1e-4					)					)






